search for: aov2

Hello, I am running error rate analysis. It is my results below. When I compare aov1 and aov2, X square = 4.05, p = 0.044, which indicates that adding the factor "Congruity" improved the fitting of model. However, the following Z value is less than 1 and p value for Z is 1, which means that "Congruity" is not significant at all. Therefore, these two parts are not consist...

...("contr.treatment", "contr.poly")) # aov: Y ~ N + V oats.aov <- aov(Y ~ N + V + Error(B/V), data = oats, qr = T) summary(oats.aov) # now lme oats.lme<-lme(Y ~ N + V, random = ~1 | B/V, data = oats) anova(oats.lme, type="m") # Ok! # aov:Y ~ N * V + Error(B/V) oats.aov2 <- aov(Y ~ N * V + Error(B/V), data = oats, qr = T) summary(oats.aov2) # now lme - my trial! oats.lme2<-lme(Y ~ N * V, random = ~1 | B/V, data = oats) anova(oats.lme2, type="m") # differences!!! (except of interaction term) My questions: 1) Is there a possibility to reproduce the r...

I'm starting to understand fixed and random effects, but I'm puzzled a bit. Here is an example from Hays's textbook (which is great at explaining fixed vs. random effects, at least to dummies like me), from the section on mixed models. You need library(nlme) in order to run it. ------ task <- gl(3,2,36) # Three tasks, a fixed effect. subj <- gl(6,6,36) # Six subjects, a random

...ot;. The effects are not unbalanced. The design is 'orthogonal'. The problem is that there are not enough degrees of freedom to estimate all those error terms. If you change the model to: aov1 <- aov(RT~fact1*fact2*fact3+Error(sub/(fact1+fact2+fact3)),data=myData) or to aov2 <- aov(RT~fact1*fact2*fact3+Error(sub/ ((fact1+fact2+fact3)^2)),data=myData) all is well. This last model (aov2) seems to me to have an excessive number of error terms. The lme model lme(RT~fact1*fact2*fact3, random=~1|sub, data=myData) is equivalent to aov0 <- aov(RT~fact1*fact2*fact...

...nding that R and SAS give different answers. Whilst the error is at the second or third decimal, the percentage error can be quite large. I'm using the effects library (Version: 2.0-10) on R?2.11.1 in the following manner: options(contrasts=c("contr.helmert","contr.poly")) aov2<-glm(log(y+.01)~covar+var1:var3+var2:var3+var1+var2+var3,data=mydat,weights=w) mod<-effect("var1",aov2) cbind(mod$fit, mod$se) R gives the following?values for var1: ??? LSM???????????? SE ?-4.080362 0.06692946 ?-4.221714 0.10233130 The same problem gives the following values in S...

...2 8 0.012365 0.9877 treat:sex 1 4 0.014175 0.9110 treat:time 2 8 0.120538 0.8880 sex:time 2 8 0.304878 0.7454 treat:sex:time 2 8 0.391012 0.6886 #### using y as dependable variable xx.lme2<-lme(y~treat*sex*time,random=~1|subject,xx) xx.aov2<-aov(y~treat*sex*time+Error(subject),xx) summary(xx.aov2) Error: subject Df Sum Sq Mean Sq F value Pr(>F) treat 1 0.147376 0.147376 2.0665 0.2239 sex 1 0.000474 0.000474 0.0067 0.9389 treat:sex 1 0.006154 0.006154 0.0863 0.7836 Residuals 4 0.285268 0.071317...

...2 8 0.012365 0.9877 treat:sex 1 4 0.014175 0.9110 treat:time 2 8 0.120538 0.8880 sex:time 2 8 0.304878 0.7454 treat:sex:time 2 8 0.391012 0.6886 #### using y as dependable variable xx.lme2<-lme(y~treat*sex*time,random=~1|subject,xx) xx.aov2<-aov(y~treat*sex*time+Error(subject),xx) summary(xx.aov2) Error: subject Df Sum Sq Mean Sq F value Pr(>F) treat 1 0.147376 0.147376 2.0665 0.2239 sex 1 0.000474 0.000474 0.0067 0.9389 treat:sex 1 0.006154 0.006154 0.0863 0.7836 Residuals 4 0.285268 0.071317...

...2 8 0.012365 0.9877 treat:sex 1 4 0.014175 0.9110 treat:time 2 8 0.120538 0.8880 sex:time 2 8 0.304878 0.7454 treat:sex:time 2 8 0.391012 0.6886 #### using y as dependable variable xx.lme2<-lme(y~treat*sex*time,random=~1|subject,xx) xx.aov2<-aov(y~treat*sex*time+Error(subject),xx) summary(xx.aov2) Error: subject Df Sum Sq Mean Sq F value Pr(>F) treat 1 0.147376 0.147376 2.0665 0.2239 sex 1 0.000474 0.000474 0.0067 0.9389 treat:sex 1 0.006154 0.006154 0.0863 0.7836 Residuals 4 0.285268 0.071317...

..."transect"=transect, "year"=year, "density"=density) Question 1: I can reproduce the results of the repeated measures anova with: >oil.aov1<-aov(density~factor(year)*factor(oiled)+Error(factor(transect)) But why is the following command not working? >oil.aov2<-aov(density~oiled*year + Error(oiled/transect), data=oil) After reading the R-help archive, as well as Chambers and Hasties (Statistical Models in S) and Pinheiro's and Bates (Mixed effects models in S and S-plus) I would expect that the correct model is the oil.aov2. As you might see f...

...1 4 0.014175 0.9110 > treat:time 2 8 0.120538 0.8880 > sex:time 2 8 0.304878 0.7454 > treat:sex:time 2 8 0.391012 0.6886 > > #### using y as dependable variable > > xx.lme2<-lme(y~treat*sex*time,random=~1|subject,xx) > xx.aov2<-aov(y~treat*sex*time+Error(subject),xx) > > summary(xx.aov2) > > Error: subject > Df Sum Sq Mean Sq F value Pr(>F) > treat 1 0.147376 0.147376 2.0665 0.2239 > sex 1 0.000474 0.000474 0.0067 0.9389 > treat:sex 1 0.006154 0.006154 0.0863 0...

I've posted the following to R-help on May 15. It has reproducible R code for real data -- and a real (academic, i.e unpaid) consultion background. I'd be glad for some insight here, mainly not for myself. In the mean time, we've learned that it is to be expected for anova(*, "marginal") to be contrast dependent, but still are glad for advice if you have experience. Thank

I am having a hard time understanding how to perform a "repeated measures" type of ANOVA with R. When reading the document found here: http://cran.r-project.org/doc/contrib/Lemon-kickstart/kr_repms.html I find that there is a reference to a function make.rm () that is supposed to rearrange a "one row per person" type of frame to a "one row per observation" type

I am having trouble getting standard errors for contrasts using se.contrast() in what appears to be a simple case to me. The following test example illustrates my problem: Lab <- factor(rep(c("1","2","3"),each=12)) Material <- factor(rep(c("A","B","C","D"),each=3,times=3)) Measurement <-

...2.048 284.24 2.149e-08 *** Residuals 7 0.050 0.007 --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 I then used model.tables to get the effects and standard errors , which gave me this: > model.tables(cd.aov2, "effects", se=T) Standard error information not returned as design is unbalanced. Standard errors can be obtained through se.contrast. ........... followed by the table of effects However, whatever I do I cannot calculate se.contrast. In the above test the factor cont has 7 levels (1,2...